Quantum Algorithm for Position Weight Matrix Matching

In this article, we propose two quantum algorithms for a problem in bioinformatics, position weight matrix (PWM) matching, which aims to find segments (sequence motifs) in a biological sequence, such as DNA and protein that have high scores defined by the PWM and are, thus, of informational importance related to biological function. The two proposed algorithms, the naive iteration method and the Monte-Carlo-based method, output matched segments, given the oracular accesses to the entries in the biological sequence and the PWM. The former uses quantum amplitude amplification (QAA) for sequence motif search, resulting in the query complexity scaling on the sequence length
$n$
, the sequence motif length
$m$
, and the number of the PWMs
$K$
as
$\widetilde{O}(m\sqrt{Kn})$
, which means speedup over existing classical algorithms with respect to
$n$
and
$K$
. The latter also uses QAA and, further, quantum Monte Carlo integration for segment score calculation, instead of iteratively operating quantum circuits for arithmetic in the naive iteration method; then, it provides the additional speedup with respect to
$m$
in some situation. As a drawback, these algorithms use quantum random access memories, and their initialization takes
$O(n)$
time. Nevertheless, our algorithms keep the advantage especially when we search matches in a sequence for many PWMs in parallel.